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IQ.Lvbs history cleanup
161
tinygrad_repo/test/opt/test_gen_float4.py
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161
tinygrad_repo/test/opt/test_gen_float4.py
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import unittest
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from tinygrad import Device, Tensor, dtypes
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from tinygrad.uop.ops import UOp, Ops
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from tinygrad.codegen import to_program
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from tinygrad.codegen.opt import Opt, OptOps
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from tinygrad.helpers import DEV
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from test.helpers import replace_opts
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AMX = "AMX" in DEV.arch
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@unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4")
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class TestFloat4(unittest.TestCase):
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@staticmethod
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def count_float4(uops: list[UOp], n=4):
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return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]),
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len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.float.vec(n)]))
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@staticmethod
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def count_half4(uops: list[UOp]):
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return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]),
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len([uop for uop in uops if uop.op is Ops.STORE and uop.src[1].dtype == dtypes.half.vec(4)]))
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def test_float4_basic(self):
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a = Tensor.empty(2, 8).realize()
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b = Tensor.empty(2, 8).realize()
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c = a + b
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s = c.schedule_linear().src[0]
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realized_ast = s.src[0]
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opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
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program = to_program(replace_opts(realized_ast, opts_to_apply), renderer=Device[Device.DEFAULT].renderer)
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assert TestFloat4.count_float4(tuple(program.src[2].src)) == (2, 1)
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@unittest.skipIf(Device.DEFAULT in {"CPU"} and AMX, "CPU with AMX upcasts float up to size 16")
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def test_float4_multidim(self):
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a = Tensor.empty(2, 8).realize()
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b = Tensor.empty(2, 8).realize()
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c = a + b
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=2)]),
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renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) == (4, 2)
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@unittest.skipUnless(Device.DEFAULT in {"CPU"} and AMX, "Only CPU with AMX upcasts float up to size 16")
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def test_float4_multidim_amx(self):
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def kernel_for_shape(size, shift):
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a = Tensor.empty(2, size).realize()
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b = Tensor.empty(2, size).realize()
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c = a + b
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s = c.schedule_linear().src[0]
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return tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=shift)]),
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renderer=Device[Device.DEFAULT].renderer).src[2].src)
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sizes = [12, 8, 16]
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shifts = [3, 2, 4]
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expected_upcast_size = [4, 8, 16]
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expected_output = [(6,3), (2,1), (2,1)]
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for i in range(len(sizes)):
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assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
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def test_float4_unaligned_load(self):
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a = Tensor.empty(9).realize().shrink(((1, 9),))
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b = Tensor.empty(9).realize().shrink(((1, 9),))
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c = a + b
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s = c.schedule_linear().src[0]
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realized_ast = s.src[0]
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opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)]
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program = to_program(replace_opts(realized_ast, opts_to_apply), renderer=Device[Device.DEFAULT].renderer)
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assert TestFloat4.count_float4(tuple(program.src[2].src)) == (0, 1)
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@unittest.skipIf(Device.DEFAULT in {"CPU"} and AMX, "CPU with AMX upcasts float up to size 16")
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def test_float4_multidim_unaligned_load(self):
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a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
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b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),))
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c = a + b
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2)]),
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renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) == (0, 2)
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@unittest.skipUnless(Device.DEFAULT in {"CPU"} and AMX, "Only CPU with AMX upcasts float up to size 16")
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def test_float4_multidim_unaligned_load_amx(self):
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def kernel_for_shape(size, shift):
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a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
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b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),))
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c = a + b
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s = c.schedule_linear().src[0]
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return tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=shift)]),
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renderer=Device[Device.DEFAULT].renderer).src[2].src)
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sizes = [13, 9, 17]
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shifts = [3, 2, 4]
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expected_upcast_size = [4, 8, 16]
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expected_output = [(0,3), (0,1), (0,1)]
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for i in range(len(sizes)):
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assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), expected_upcast_size[i]) == expected_output[i]
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def test_float4_sometimes_unaligned(self):
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a = Tensor.empty(1, 1, 8).realize()
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b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
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c = a.conv2d(b)
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# only the first and last conv dot products are aligned in a, and b is never aligned, so no
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# float4 should be emitted (the reduce axis of size 4 is the float4 axis here)
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UNROLL, axis=0, arg=4)]), renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) == (0, 0)
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def test_float4_multidim_sometimes_unaligned(self):
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a = Tensor.empty(1, 1, 7).realize()
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b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5)))
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c = a.conv2d(b)
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# the first conv dot product is aligned in a. If we upcast the output and reduce
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# dimension, then we could do float4 for only that one set of loads, but we currently
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# don't.
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# UPDATE: now we do this fusion
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=0)]),
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renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) in {(0,1), (1,1)}
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def test_float4_expand(self):
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a = Tensor.empty(9).realize().shrink(((1, 9),))
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b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,))
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c = a + b
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# we will upcast the top axis of sz 4. they should not be coalesced into float4,
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# since the top axis is not contiguous.
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=0, arg=4)]), renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) == (0, 1)
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def test_float4_heterogeneous(self):
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a = Tensor.empty(8).realize()
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b = Tensor.empty(9).realize().shrink(((1, 9),))
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c = a + b
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# should float4 b but not a
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s = c.schedule_linear().src[0]
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uops = tuple(to_program(replace_opts(s.src[0], [Opt(op=OptOps.UPCAST, axis=0, arg=4)]), renderer=Device[Device.DEFAULT].renderer).src[2].src)
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assert TestFloat4.count_float4(uops) == (1, 1)
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if __name__ == '__main__':
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unittest.main()
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